The vision transformer (ViT) has become a hot topic in image processing due to its global feature extraction capabilities. However, the ViT suffers from over-smoothing in feature extraction and over-fitting in the training procedure, so it is hard to achieve satisfactory performance in hyperspectral image (HSI) classification. To address these issues, we propose a ViT with contrastive learning (CViT). The network architecture includes a patch embedding module, transformer blocks, and a classifier. The training of CViT can be considered as an optimization problem with a supervised contrastive loss, an unsupervised contrastive loss, and an ℓ ₁ -regularizer with respect to linear self-attention weights. Specifically, the supervised contrastive loss is proposed to alleviate the negative effects of HSI features’ spectral variability and spatial diversity by increasing intra-class consistency. On the other hand, the unsupervised contrastive loss is exploited to reduce redundancy by reconstructing global structural information. In particular, regularized linear self-attention weights reduce the over-smoothing issue. Extensive experimental results on three HSI datasets demonstrate that the proposed CViT achieves competitive performance.

Pixel‐wise classification of hyperspectral image (HSI) is a hot spot in the field of remote sensing. The classification of HSI requires the model to be more sensitive to dense features, which is quite different from the modelling requirements of traditional image classification tasks. Cycle‐Multilayer Perceptron (MLP) has achieved satisfactory results in dense feature prediction tasks because it is an expert in extracting high‐resolution features. In order to obtain a more stable receptive field and enhance the effect of feature extraction in multiple directions, we propose an MLP‐like model called DriftNet for HSI classification inspired by Cycle‐MLP and deformable convolution. Besides, the proposed DriftNet uses a unique ladder‐like fully connected structure to achieve progressive activation of neurons and facilitates the fusion of spatial and spectral information, thereby obtaining more sensitive location information for better classification results. Experimental results on three public hyperspectral datasets demonstrate the effectiveness and generalisation of DriftNet.

Well log data play a crucial role in lithology identification, given their nature as petrophysical measurements of stratigraphy that reflect lithological successions and depositional processes. Extracting recognizable features from well logs has become a focal point in lithology identification tasks. Recent advancements in machine learning have improved feature extraction by incorporating neighborhood information, particularly through filtering techniques that integrate sequence dependencies from well logs. However, traditional filters are often constrained by predefined theoretical models, posing adaptability challenges across varied reservoir conditions. To address these limitations, we propose a novel Category-wise Sequence Correlation (CSC) filter for lithology identification, which is data-driven and model-free. Notably, the CSC filter captures the sequence structural variations arising from category heterogeneity by utilizing labeling information derived from well logs. Building on this, the Category-wise Sequence Correlation Filter Banks (CSCFB) model is developed. It uses a series of CSC filter banks as one-dimensional convolution templates acting on each logging curve to extract discriminative features associated with lithological categories. Application studies on several oil and gas fields demonstrate that the CSCFB model outperforms competitors in classification performance, proving its effectiveness and utility across diverse reservoir types.

In this study, a molecularly imprinted polymer (MIP) that can selectively recognize both the trichlorfon and the structurally related compound of monocrotophos pesticides was prepared using mixtures of trichlorfon and monocrotophos as template molecules, methacrylic acid (MAA) as a functional monomer, and ethylene glycol dimethacrylate as a cross-linker. The polymerization conditions had an important influence on the adsorption ability of imprinted polymer. When the ratio of trichlorfon and monocrotophos was 1.5:1 (mol/mol), the MIP had high adsorption capacity. The prepared polymers were characterized by Fourier transform infrared spectroscopy and static and kinetic adsorption experiments. The results indicated that the P = O groups of trichlorfon and monocrotophos reacted with the -OH groups of MAA during the synthesis of MIP. The imprinted polymer exhibited good recognition ability and higher selectivity toward trichlorfon and monocrotophos than the structurally related pesticide compounds of acephate and methamidophos, and offered fast kinetics for solid phase by the MIP adsorption of trichlorfon and monocrotophos from liquid phase.